Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "104" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 22 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 22 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.361207 | 51.733933 | -0.318861 | 6.753470 | 2.555919 | 0.364266 | 2.383689 | 0.192298 | 0.6272 | 0.6161 | 0.3655 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.922363 | 56.422646 | -0.170970 | 7.293430 | 3.545161 | -0.088497 | 2.307273 | 0.787642 | 0.6368 | 0.6314 | 0.3639 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.402868 | 60.077864 | 0.894917 | 9.065462 | 2.458458 | 0.047686 | 1.372575 | 1.183063 | 0.6445 | 0.6350 | 0.3765 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 99.46% | 99.41% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.4622 | 0.4916 | 0.4160 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 238.419207 | 237.393747 | inf | inf | 2250.161604 | 2334.088004 | 4860.106765 | 5330.502673 | nan | nan | nan | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.393217 | 70.744085 | -0.951861 | 7.151882 | 3.103983 | -0.073493 | 0.134600 | 0.258084 | 0.6445 | 0.6273 | 0.3740 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.567207 | 58.200781 | -1.045533 | 6.982729 | 3.226063 | -0.356952 | 0.714763 | 1.729350 | 0.6408 | 0.6241 | 0.3692 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.150994 | 59.638209 | -0.906636 | 6.474581 | 2.344901 | -0.688741 | -0.055128 | -0.052370 | 0.6355 | 0.6226 | 0.3707 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.139455 | 69.873941 | -1.083889 | 7.105368 | 3.153034 | -0.437819 | 0.318684 | 0.659346 | 0.6359 | 0.6265 | 0.3641 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.827809 | 58.516281 | -0.764033 | 7.027324 | 0.578827 | -0.284752 | 0.758865 | 0.521978 | 0.6468 | 0.6307 | 0.3630 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.936467 | 70.085667 | -0.894388 | 7.487128 | 2.361753 | -0.416600 | -0.640576 | 2.104980 | 0.6662 | 0.6605 | 0.3177 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.380658 | 65.899868 | -0.709090 | 7.126842 | 0.542941 | -0.551961 | 0.427609 | 1.582580 | 0.6452 | 0.6257 | 0.3724 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.669874 | 61.458419 | -0.448707 | 7.787141 | 1.775766 | -0.061968 | 0.239374 | 4.263568 | 0.6590 | 0.6386 | 0.3476 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.673918 | 60.981395 | -1.098378 | 7.002298 | 1.402943 | -0.463431 | 0.879439 | 1.850389 | 0.6715 | 0.6741 | 0.3020 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 0.22% | 0.00% | 0.00% | - | - | 10.075884 | 39.577930 | -1.051096 | 6.137870 | 0.889696 | 3.965903 | -0.361376 | 4.911878 | 0.7204 | 0.7157 | 0.2604 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 15.18% | 0.49% | 0.00% | - | - | 12.222619 | 49.642936 | -1.156435 | 6.956979 | 20.269734 | 51.654386 | 189.240156 | 390.918017 | 0.5358 | 0.5923 | 0.3377 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 12.750149 | 53.787093 | -0.734491 | 6.662044 | 1.497027 | 0.618225 | -0.560269 | 5.538463 | 0.6885 | 0.6837 | 0.2825 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.384453 | 57.929381 | -1.073415 | 6.451733 | 1.372709 | -0.781788 | 1.240520 | 2.287713 | 0.6366 | 0.6234 | 0.3605 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.345338 | 59.189508 | -1.059693 | 6.892504 | 0.839308 | -0.431266 | 0.997079 | 3.289932 | 0.6384 | 0.6222 | 0.3686 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.691957 | 58.289027 | -0.784913 | 6.657655 | 2.846134 | -0.236224 | 0.805505 | 3.666400 | 0.6080 | 0.5935 | 0.3350 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.139974 | 58.588689 | -0.948318 | 7.073395 | 3.043151 | -0.071239 | 0.425323 | 1.241431 | 0.6435 | 0.6272 | 0.3619 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 51.733933 | 6.361207 | 51.733933 | -0.318861 | 6.753470 | 2.555919 | 0.364266 | 2.383689 | 0.192298 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 56.422646 | 5.922363 | 56.422646 | -0.170970 | 7.293430 | 3.545161 | -0.088497 | 2.307273 | 0.787642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 60.077864 | 6.402868 | 60.077864 | 0.894917 | 9.065462 | 2.458458 | 0.047686 | 1.372575 | 1.183063 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | ee Power | inf | 238.419207 | 237.393747 | inf | inf | 2250.161604 | 2334.088004 | 4860.106765 | 5330.502673 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 70.744085 | 7.393217 | 70.744085 | -0.951861 | 7.151882 | 3.103983 | -0.073493 | 0.134600 | 0.258084 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 58.200781 | 58.200781 | 6.567207 | 6.982729 | -1.045533 | -0.356952 | 3.226063 | 1.729350 | 0.714763 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 59.638209 | 59.638209 | 6.150994 | 6.474581 | -0.906636 | -0.688741 | 2.344901 | -0.052370 | -0.055128 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 69.873941 | 69.873941 | 7.139455 | 7.105368 | -1.083889 | -0.437819 | 3.153034 | 0.659346 | 0.318684 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 58.516281 | 5.827809 | 58.516281 | -0.764033 | 7.027324 | 0.578827 | -0.284752 | 0.758865 | 0.521978 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 70.085667 | 70.085667 | 6.936467 | 7.487128 | -0.894388 | -0.416600 | 2.361753 | 2.104980 | -0.640576 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 65.899868 | 65.899868 | 6.380658 | 7.126842 | -0.709090 | -0.551961 | 0.542941 | 1.582580 | 0.427609 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 61.458419 | 5.669874 | 61.458419 | -0.448707 | 7.787141 | 1.775766 | -0.061968 | 0.239374 | 4.263568 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 60.981395 | 10.673918 | 60.981395 | -1.098378 | 7.002298 | 1.402943 | -0.463431 | 0.879439 | 1.850389 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 39.577930 | 10.075884 | 39.577930 | -1.051096 | 6.137870 | 0.889696 | 3.965903 | -0.361376 | 4.911878 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Temporal Discontinuties | 390.918017 | 49.642936 | 12.222619 | 6.956979 | -1.156435 | 51.654386 | 20.269734 | 390.918017 | 189.240156 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 53.787093 | 53.787093 | 12.750149 | 6.662044 | -0.734491 | 0.618225 | 1.497027 | 5.538463 | -0.560269 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 57.929381 | 11.384453 | 57.929381 | -1.073415 | 6.451733 | 1.372709 | -0.781788 | 1.240520 | 2.287713 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 59.189508 | 59.189508 | 10.345338 | 6.892504 | -1.059693 | -0.431266 | 0.839308 | 3.289932 | 0.997079 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 58.289027 | 8.691957 | 58.289027 | -0.784913 | 6.657655 | 2.846134 | -0.236224 | 0.805505 | 3.666400 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 104 | N08 | RF_maintenance | nn Shape | 58.588689 | 58.588689 | 9.139974 | 7.073395 | -0.948318 | -0.071239 | 3.043151 | 1.241431 | 0.425323 |